the impact of turkish manufacturing industry on co2 …

LAÜ Sosyal Bilimler Dergisi (VIII-II) EUL Journal of Social Sciences

Aralık 2017 December

LAÜ Sosyal Bilimler Dergisi (VIII-II): 150-167

THE IMPACT OF TURKISH MANUFACTURING INDUSTRY

ON CO2 EMISSIONS

TÜRKİYE’DE İMALAT SANAYİ SEKTÖRÜNÜN CO2 EMİSYONU ÜZERİNE ETKİSİ

Zeliha ÖZDEMİR Asst. Prof. Dr. Demet Beton KALMAZ

European University of Lefke European University of Lefke

MBA graduate Department of Economics

[email protected] [email protected]

Received 5 May 2017- Accepted 22 August 2017

Gönderim 5 Mayıs 2017- Kabul 22 Ağustos 2017

Abstract: Global warming has emerged as a result of human activities, and greenhouse gases are

the most effective cause of global warming. The accumulations of greenhouse gases have rapidly

increased in the atmosphere as a result of the increase in use of fossil fuels, wrong use of land, and

industrialization. The aim of the study is to examine the relationship between carbon dioxide

emissions, electricity consumption, economic growth, and trade openness on manufacturing

industry in Turkey by using time series data between the years 1962 and 2013. The study employs

the bound test for ARDL approach, ECM, Granger causality test and Cusum and Cusumsq test.

CO2 emissions are high due to two reasons in Turkey. First, Turkey’s energy intensity is high.

Second, electricity production depends on non-renewable sources. Turkey should decrease energy

intensity while increasing the share of renewable energy sources for energy production to be able to

decrease energy CO2 emissions in the manufacturing industry.

Keywords: CO2 emissions, electricity consumption, trade, ARDL

Öz: Günümüzün en önemli sorunlarında biri haline gelen küresel ısınma insan faaliyetleri sonucu atmosferde bulunan sera gazlarının artması sonucu ortaya çıkmıştır. Küresel ısınmanın en önemli

sebebi olan sera gazlarının atmosferde artması özellikle sanayi devrimi ile birlikte fosil yakıtlarına olan talebin artmasına ve yanlış arazi kullanımına bağlanmaktadır. Atmosferde bulunan sera

gazları içerisinde küresel ısınmaya en fazla etki eden 𝑪𝑶𝟐 emisyonudur. Türkiye'de bulunan imalat

sanayi sektörleri üzerinde yapılan bu çalışmanın amacı, karbondioksit emisyonları, elektrik tüketimi, ekonomik büyüme ve imalat sanayi sektöründeki ticaret açıklığı arasındaki ilişkiyi 1962

ve 2013 yılları arasında, zaman serisi verilerini kullanarak incelemektir. Araştırmada, ARDL sınır testi yaklaşımı, hata düzeltme modeli, Granger nedensellik testi, ve cusum ve cusumsq testi

uygulanmıştır. Türkiye'de 𝑪𝑶𝟐 emisyonunun yüksek olmasının iki nedeni vardır. Birincisi,

Türkiye'nin enerji yoğunluğunun yüksek olması, ikincisi ise Türkiye’de elektrik üretiminin büyük

bir bölümünün yenilenemez enerji kaynaklarına bağlı olmasıdır. Bu nedenle, Türkiye’de 𝑪𝑶𝟐

emisyonlarını azaltabilmek için enerji üretiminde yenilenebilir enerji kaynaklarının payı arttırılmalı ve imalat sanayinde enerji yoğunluğu azaltılmalıdır.

Anahtar Kelimeler: CO2 emisyonu, elektrik tüketimi, ticaret, ARDL

mailto:[email protected]

mailto:[email protected]



Zeliha Özdemir, Demet Beton Kalmaz | 151

INTRODUCTION

Earth's atmosphere consists of various gases and these gases traps heat which

creates greenhouse effect. The increase of these atmospheric gases causes the

problem of global warming. Since these gases create greenhouse effect, they are

called greenhouse gases (GHG). Global warming, which arises as a result of GHG, is

one of the main concerns of the recent scientists and policy makers. Global warming

is the increase of the temperature of land, sea and air (Dincer, 2000). As the global

warming increases, the sea level rises and the glaciers melt. The risk of flood

increases.

GHG consist of carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O),

chlorofluorocarbons (CFC), hydrofluorocarbons (HCFC) and Sulphur hexafluoride

(SF6). Table 1 represents the properties of the GHG in the atmosphere.

Table 1: Properties of GHG

GHG

Anthropogenic

Sources

Concentration

(parts per

billion)

Global

Warming

Potential

(GWP)

Lifetime in

Atmosphere

(year)

Carbon Dioxide

(CO2)

Fossil-fuel combustion,

Cement production Land-

use conversion,

280,000

1

100

Methane

(CH4)

Fossil-fuel, Rice paddies,

Waste dumps 700 25 12

Nitrous Oxide

(N2O) Fertilizer, Combustion,

Industrial processes 270 298 114

Chlorofluoroca

rbons (CFC) Liquid coolants, Foams 0,534 10,900 100

Hydrofluorocar

bons (HCFC) Refrigerants 0,218 1,810 12

Sulphur

Hexafluoride (SF6) Dielectric fluid 0,00712 22,800 3,200

Source: Center for Climate and Energy Solutions, 2017

*Global Warming Potential for 100-time horizon.

In the first column of Table 1, the names of GHG in atmosphere are given,

followed by a column giving information about the anthropogenic sources of the

GHG. The third column influences the concentration of each GHG which influences

the intensity of gases in the atmosphere. Fourth column represents the global

warming potential (GWP) of the related GHG, and finally in the last column gives

the information for how long the gas remains in the atmosphere as measured in years.

Carbon Dioxide (CO2) occurs as a result of fossil-fuel combustion, cement

production and land use conversion. Atmospheric concentration of CO2 is 280,000

per billion. GWP is taken as constant in CO2 and it remains 100 years in atmosphere.

Methane (CH4) is caused by fossil-fuels, rice paddies and waste dumps. From Table

1, it can be seen that a CH4 gas has twenty five times GWP more than CO2.

Atmospheric concentration of CH4 is 700 per billion and lifetime is 12 years in

https://en.wikipedia.org/wiki/Chlorofluorocarbon




152 | The Impact of Turkish Manufacturing Industry on CO2 Emissions

atmosphere. Nitrous Oxide (N2O) comes from fertilizers, fossil-fuel combustion and

industrial processes. Table 1 shows that N2O has 298 times GWP more than CO2.

N2O remains for 114 years and its concentration is 270 per billion in atmosphere.

Chlorofluorocarbons (CFC) consist of liquid coolants and foams. CFC atmospheric

concentration is 0,534 per billion. CFC has 10,900 times GWP more than CO2 and

remains 100 years in the atmosphere. Hydrofluorocarbons (HFCF) is a refrigerant

gases, it has 1,810 times GWP more than CO2. Atmospheric concentration of HFCF is 0,218 per billion and it remain 12 year in atmosphere. The last listed Sulphur

Hexafluoride (SF6) is used to cut the electricity. SF6 comes from dielectric fluid, SF6 22,800% times global warming potential more than CO2. Atmospheric concentration

of SF6 is 0,00712 per billion. SF6 has 3,200 years lifetime in atmosphere (Center for

Climate and Energy Solutions, 2017). All gases have heat-trapping effect but CO2 has highest heat-trapping ability among other GHG in the atmosphere (Özmen,

2009).

Turkey have growing rate of population and urbanization, so that Turkey’s

energy needs are increasing in all sectors (Bilgen et al., 2008). As the need for

energy is increasing, both energy consumption and energy production are also

increasing in Turkey. The increase of energy use increases also GHG generated in

Turkey. GHG generation of Turkey increased from 134,4 million tons to 340 million

tons between the years 1990 and 2015 (Turk Stat, 2017). Turkey contributes to more

GHG emissions during the production of energy process and this energy are mostly

used for manufacturing industry. Therefore, the impact of the economic variables

that causes CO2 emissions in Turkey is investigated in this study. This study will be

guidance for further research on topic and the results obtained from the empirical

analysis can be used to shape the environmental policies to decrease CO2 emissions

in Turkey.

1. LITERATURE REVIEW

The previous studies in literature related to the topic aimed to find the

relationship between CO2 emissions, energy consumption and economic growth

(Acaravcı & Ozturk, 2010; Pao et al., 2012; Al-mulali, 2011; Apergis & Payne,

2009; Bozkurt & Akan, 2014; Chang, 2010; Çoban & Kılınç, 2015; Ergün & Polat,

2015; Alam et al., 2012; Koçak, 2014; Lean & Smyth, 2010; Tiwari, 2011; Pao &

Tsai, 2010; Yavuz, 2014; Wang et al., 2011). In some studies in addition to those

variables FDI is also included (Altıntaş, 2013; Dinh & Shih-Mo, 2014; Öztürk & Öz,

2016; Tang & Tan, 2015). There are also studies adding trade openness instead of

FDI (Akın, 2014; Halıcıoğlu, 2009; Jalil & Mahmud, 2009; Keskingöz &

Karamelikli, 2015; Kivyiro & Arminen, 2014).

There are also studies focusing the causal interaction between CO2 emissions,

energy consumption, economic growth (Çoban & Kılınç, 2015) in Turkey. Some of

these studies included FDI as a control variable (Altıntaş, 2013; Öztürk & Öz, 2016),

while some added trade openness instead of FDI (Halıcıoğlu, 2009). There are also

studies adding employment ratio instead of trade openness (Öztürk & Acaravcı,

2010). Some studies found unidirectional causal relationship from energy

consumption to CO2emissions (Altıntaş, 2013; Çoban & Kılınç, 2015), while some






studies estimated unidirectional causal relationship from economic growth to

CO2 emissions (Altıntaş, 2013). Some authors found bidirectional causality between

economic growth and CO2 emissions (Halıcıoğlu, 2009; Öztürk & Öz, 2016). Öztürk

& Acaravcı (2010) found no causal relationship among the variables. Some studies

applied only cointegration test on CO2 emissions and economic variables (Bozkurt &

Akan, 2014; Bozkurt & Okumuş, 2015; Çetin & Şeker, 2014; Keskingöz &

Karamelikli, 2015; Koçak, 2014; Yavuz, 2014). The results of those studies found

energy consumption, economic growth and CO2 emissions are cointegrated.

Table 2, summarizes some of the literature by giving the names of the authors,

the country studied, the sample used, model employed and the results found.

Table 2: Literature Review Summary

Authors Country Sample Variable Model Result

Acaravcı &

Öztürk (2010)

Nine

European

Countries

1960-2005

CO2 , EC ,EG

ARDL,

Granger Causality

Test

EG → CO2

EC → CO2

Adom et al.

(2012)

Ghana, Senegal

and

Morocco

1971-2007

CO2 , EG, TE,

IS

Bounds,

Granger Causality

Test

CO2 ↔ EG CO2 ↔ IS CO2 ↔ TE

Akbostancı et al.

(2009) Turkey 1968-2003

CO2, EG, POP

EKC,

Cointegration CO2 andEG

cointegration

Akın (

2014)

85 countries

1990-2011

CO2, EG, EC, TO

Cointegration,

Granger Causality

Test

CO2 → TO EG → CO2

EG → TO

Al-mulali (2011) MENA countries

1980-2009 CO2 , EG, EC Granger

Causality Test

CO2 ↔ EC CO2 ↔ EG

Al-mulali et al.

(2012) Seven region 1980-2008 CO2 ,UR, EC

Granger

Causality Test CO2 ↔ UR CO2 ↔ EC

Altıntaş (2013)

Turkey

1970-2008 CO2, EG, EC,

FDI

ARDL,

Granger Causality

Test

EG → CO2

EC → CO2

Anatasia

(2015)

Thailand Malaysia

1978-2008

CO2, EG, EC,

Export

Cointegration,

Granger Causality

Test

Export→ CO2

EG → CO2

EG → EC

Apergis & Payne

(2009)

Central America

1971-2004 CO2 , EG, EC,

EKC,

Granger Causality

Test

EC → CO2

EG → CO2

Ayeche et al.

(2016)

40 European

countries

1985-2014

EG, FD, TO,

CO2, EC, FDI, INF,

UR

ARDL,

Granger Causality

Test

EG ↔ FD EG ↔ TO

Aytun & Akın (2015)

Turkey 1971-2010 PSE, SSE,

TSE, CO2

Granger Causality Test

TSE→CO2

Bozkurt & Akan

(2014) Turkey 1960-2010 EC, CO2, EG

Cointegration

Test EC is positive

effect on CO2

Bozkurt &

Okumuş (2015)

Turkey

1966-2011

CO2, EG, EC, POP, TO

Cointegration

Test

All variable

cointegration

Chang

(2010)

China

1982-2004

CO2,EG , EC

Cointegration,

VECM ,Granger

Causality Test

EG → CO2

Cowan et al.

(2014)

BRICS

1990-2010

CO2, EG,

electricity

consumption

Granger

Causality Test

EG → CO2

electricity

consumption

→ CO2

Çetin & Sub Saharan 1985-2010 CO2 , EC, UR VECM, CO2 ↔ EC




Ecevit (2015) Countries Granger Causality

Test CO2 ↔ UR

Çetin & Seker (2014)

Turkey 1980-2010 CO2, EC, TO ARDL All variable

cointegration

Çetintaş &

Sarıkaya (2015)

USA and UK

1960-2004

CO2, EG, EC, TO, UR

Cointegration,

Granger Causality

Test

CO2→ EG EC → CO2

Çoban & Kılınç

(2015)

Turkey

1990-2012 CO2,

EG, EC

Cointegration,

Granger Causality

Test

EC → CO2

Dinh & Shih-Mo

(2014)

Vietnam

1980-2010 CO2, EC, EG,

FDI

Cointegration,


CO2 ↔ EC

Dogan &

Turkekul (2016)

USA

1960-2010 CO2, EG, EC

, TO, UR

Cointegration,

Granger Causality

Test

CO2 ↔ EG

CO2 ↔ EC CO2 ↔ UR

Ergün & Polat

(2015)

30 OECD

countries

1980-2010

CO2, EC, EG

Cointegration,

Granger Causality

Test

EG → CO2

Halıcıoğlu (2009)

Turkey

1960-2005 CO2, EC,EG,

TO

ARDL,

Granger Causality

Test

CO2 ↔ EC CO2 ↔EG

Hossain (2011)

NIC

1971-2007 CO2, EC, TO, EG, UR

Cointegration,


EC → CO2

TO → CO2

Hossain (2012)

Japan

1960-2009

CO2 , EG, EC, TO, UR

Cointegration,

Granger Causality

Test

CO2→ EG EC → CO2

Jalil & Mahmud

(2009)

China

1975-2005

CO2 , EG, EC, TO

EKC, Granger

Causality Test

EG → CO2

Kapusuzoğl

u

(2014)

Worldwide

OECD EU

Turkey

1960-208

CO2 , EG

Cointegration,

Granger Causality

Test

CO2→ EG

Kasman &

Duman

(2015)

New EU

member and

candidate countries

1992-2010

CO2, EC, EG, TO, UR

EKC, Granger

Causality Test

EC → CO2

EG → CO2

UR → CO2

TO → CO2

UR → TO

Katırcıoğlu

(2017)

Turkey

1960-2010

CO2, EC, EG,

Oil price

Cointegration

ECM,

Granger

Causality Test

EC → Oil

price

Katırcıoğlu,et al.

(2014)

Cyprus

1970-2009

CO2, EC, IT

Bounds test,

Granger Causality

test

CO2 ↔ EC IT→ CO2

EC →IT

Keskingöz

& Karamelikli (2015)

Turkey

1960-2011 CO2, EG, EC,

TO

ARDL

Import has

positive effect on CO2

Kivyiro &

Arminen (2014)

Six Sub

Saharan Africa

1971-2009

CO2, EG, EC, TO

ARDL,

Granger Causality

Test

CO2→ EG

Koçak (2014) Turkey 1960-2010 CO2, EG, EC ARDL EC positive

effect on CO2

Lean & Smyth

(2010)

ASEAN

1980-2006

CO2, EG,

electricity

consumption

EKC, Granger

Causality Test

CO2→

electricity

consumption CO2→ EG

Mohapatra & Giri

(2015)

India

1971-2012 CO2, EG, EC,

TO, UR, GFCF

ARDL,

Granger Causality

Test

EC → CO2

Narayan &Narayan (2010)

Malaysia 1980-2004 CO2, EG Cointegration EG effects on

CO2

Niu et al. (2011) Eight Asian-

Pasific

1971-2005

CO2, EG, EC

Cointegration,


CO2→ EG




Öztürk &

Acaravcı (2010)

Turkey

1968-2005 CO2, EG, EC,

LF

ARDL,

Granger Causality

Test

All variable

co-integrate

Öztürk & Öz

(2016)

Turkey

1974-2011 CO2, EG, EC,

FDI

Cointegration,


EC → EG CO2→ EC

Pao & Tsai

(2010)

BRIC countries

1971-2005 CO2, EG, EC EKC,

Granger Causality CO2 ↔ EC CO2 → EG

Salahuddin et al.

(2015)

Gulf

Cooperation

Council

1980-2012

CO2, EG, EC, FD

Cointegration,

Granger Causality

EC → CO2

CO2 ↔ EG

Tang & Tan

(2015) Vietnam 1976-2009

CO2, EG, EC,

FDI

EKC, Granger

Causality Test CO2 ↔ EG CO2 ↔ EC

Tiwari

(2011)

India

1971-2005

CO2, EG, EC

Cointegration,


CO2 → EG CO2 ↔ EC

Xiongling (2016)

China

1961-2010

CO2, EG

Cointegration,


EG → CO2

Yavuz (2014) Turkey 1960-2007 CO2, EG, EC EKC,

Cointegration CO2, EG and

ECcointegration

Wang et al. (2011)

China 1995-2007 CO2, EG, EC Granger

Causality Test CO2 ↔ EC

CO2: Carbon Dioxide Emissions PSE: Primary School Enrollment POP: Population

EC: Energy Consumption SSE: Secondary School Enrollment UR: Urbanization EG: Economic Growth TSE: Tertiary School Enrollment LF: Labor Force

TO: Trade Openness GFCF: Gross Fixed Capital Formation INF: Inflation Rate

IT: International Tourism TE: Technical Efficiency

FDI: Foreign Direct Investment FD: Financial Development

Different results appear in different studies because of the different variables,

different models, different data, and different methods used and different countries

included. In those studies the mostly used methods are ARDL and Granger causality

tests in both cross country panel studies and time series estimations.

We used electricity consumption instead of energy consumption since electricity

consumption has the highest share in total energy consumption in Turkey. Also the

shares of imports and exports of manufacturing industries in GDP is used instead of

the share of total imports and exports in GDP since manufacturing industry to be able

to investigate the impact of the manufacturing trade on CO2 emissions. To our

knowledge, there is no study in the literature integrating those variables in one

analysis. It should also be mentioned that the data used in this study covers longer

time period than the previous studies applied for the case of Turkey.

2. DATA AND METHODOLOGY

In this paper, the impact of electricity consumption, economic growth and trade

openness on CO2 emissions in Turkey by employing a time series data covering the

years from 1962 to 2013 collected from World Bank Development Indicators. The

long-run interaction between the dependent (CO2) and independent variables are

estimated by employing the econometric model represented by Equation (1). All

variables are in natural logarithms.

lnCO2= β0 + β1lnEC + β2lnGDP + β3lnTO + ɛ (1)




In equation (1) β0 is the constant term and β1, β2, β3 are the long-run elasticities

with respect to electric power consumption, per capita GDP and trade openness. ɛ is

the error term and ln indicates that general logarithmic form of the variables. CO2 denotes carbon dioxide emissions per capita, while EC is electric power consumption

(kWh per capita) which is equal to total net electricity generation plus electricity

imports minus electricity exports minus electricity distribution losses. GDP is real

GDP per capita calculated by the 2005 US$ prices. GDP calculated as GDP divided

by population. To indicate trade openness which is calculated as the sum of

percentage of manufacturing exports and percentage of manufacturing imports to

GDP as suggested by Yanıkkaya (2003) and the subscript t represents the time

period.

3. EMPIRICAL ANALYSES

3.1. Unit Root Tests

In the time series analyzes, firstly, the stability levels of the series should be

determined. Non-stationary creates problem of spurious regression (Granger &

Newbold, 1974). So, Augmented Dickey Fuller (ADF) (1981), Phillips-Perron (PP)

(1988) and Kwiatkowski-Phillips-Schmidt-Shin (KPSS) (1992) unit root tests are

applied to test for stationarity of the series. Table 3 illustrates test results of

stationarity of ADF, PP and KPSS for CO2 emissions, GDP, electricity consumption

and trade openness.

Table 3: Unit Root Test Result: with intercept

Variables lnCO2 lnEC lnGDP lnTO

ADF I(1)* I(1)* I(1)* I(1)*

PP I(1)* I(1)* I(1)* I(1)*

KPSS I(1) I(0) I(1) I(1)

* and ** denotes the statistical significance at the 1% and 5%, levels respectively.

ln CO2, lnEC, lnGDP and lnTO are stationary both in ADF and PP tests at I(1).

According to KPSS test results; all variables are stationary at mix levels. Since

variables are stationary at mix level, ARDL approach is applied under bounds test in

this study the investigate long run relationship among variables.

3.2. Bounds Test

Bounds test is developed by Pesaran and Shin (1995), Pesaran et al., (1999,

2001) under the ARDL approach which estimates the long run interaction among the

variables. If all variables are stationary at same level, Engle-Granger (1987),

Johansen (1988, 1991) and Johansen-Juselius (1990) cointegration tests can be

applied to determine the long-run relationship between the variables. Pesaran et al.

(2001) proposed the ARDL approach for testing the relationship between different

levels of integrated variables (Verma, 2007; Esen, et al., 2012).

The ARDL approach is based on the least squares method. The main advantage

of the ARDL model is that with mix order of integration of variables at either I (1) or

I (0) it can be applied for cointegration test and the obtained results will be




𝑖=1 𝑖=0

meaningful. In this study the variables are stationary at mix levels so that ARDL test

is employed to estimate long run interaction among variables.

According to the unit root test results the regressors have mixed ordered of

integration, therefore, Bounds tests based on ARDL approach represented by the

following model will be estimated with this respect:

∆ln𝐶𝑂2𝑡 = 𝛼0 + ∑𝑝 𝛼1𝑖 ln𝐶𝑂2𝑡−𝑖

𝑝 𝑖=0 𝛼2𝑖 ln𝐸𝐶 𝑡−𝑖 + ∑𝑝

𝛼3𝑖ln𝐺𝐷𝑃𝑡−𝑖 + 𝑝 𝑖=0 𝛼4𝑖 ln𝑇𝑂 𝑡−𝑖 + 𝜀𝑡 (2)

Where, α’s are the dynamic coefficients of the attached variables in long-run and

εt is an error term.

ARDL approach under the Bounds test results follows F-distribution. The critical

values for Bounds test is developed by Narayan and Narayan (2005) and Pesaran and

Timmermann (2005) which follow F distribution. If F-statistics for bounds test

exceeds the upper bound of critical value the null of no long run relationship between

the variables is rejected. If F-statistics for bounds test is estimated to be lower than

the lower bound of critical value the null hypothesis is not rejected. If F-statistics for

bounds test falls between lower and upper bounds than the analysis is said to be

inconclusive. This is stage one is necessary step to check whether exist a long run

relationship between the variables under investigation is tested by computing F-

statistics for the significance of the lagged levels of the variables in the error

correction form of the underlying ARDL model. The F-statistics confirms that there

is a cointegrating relationship based on the three models.

Bounds test considers five different cases. First case is applied without intercept

and trend, second case is applied with restricted intercept and without trend, Third

case is applied without deterministic unrestricted intercept and without trend, Fourth

case is applied by including the deterministic unrestricted intercept and restricted

trend and the last case is applied with unrestricted intercept and restricted trend. In

this study, we focused on the results of only the last three since they give more

reliable results (Katırcıoğlu et al., 2013).

Table 4 shows critical values of bounds test and table 5 shows the bounds test for

level relationship among variables.

Table 4: Critical Values of Bounds Test

Significance I(0) I(1)

0.01 3.65 4.66

0.05 2.79 3.67

0.10 2.37 3.2

F statistic : 7.9588

+ ∑

∑




𝑖=1 𝑖=0

Table 5: The Bounds Test for level relationship

Without deterministic

unrestricted intercept and no trend

With deterministic

unrestricted intercept and restricted trend

With unrestricted

intercept and restricted trend

Conclusion

Variables 𝐹𝑖𝑖𝑖 𝐹𝑖𝑣 𝐹𝑣 𝐻0

CO2/ EC, GDP, TO

2.700** 2.303*** 2.864** Rejected

** and *** denotes the statistical significance at the 5% and %10.

According to the Bounds test results, there is a long run relationship exist among

variables, which indicates EC, GDP and TO are in long run relationship with CO2 for

Turkey.

3.3. Error Correction Model

Error Correction Model (ECM) is developed by Engle-Granger (1987) stated

short-term imbalances when there is cointegration of the variables are established.

ECM coefficient shows the short run deviation from its long run equilibrium. ECM is

given below;

∆ln𝐶𝑂2𝑡 = 𝜆0 + ∑𝑝 𝜆1𝑖 ∆ln𝐶𝑂2𝑡−1

𝑝 𝑖=0 𝜆2𝑖 ∆ln𝐸𝐶 𝑡−1 + ∑𝑝 𝜆3𝑖 ∆ln𝐺𝐷𝑃𝑡−1

𝑝 𝑖=0 𝜆4𝑖 ∆ln𝑇𝑂 𝑡−1 + 𝜑𝐸𝐶𝑇𝑡−1 + 𝜁𝑡 (3)

Where λ is the parameter of error correction, ∆ is indicates the first differences

while the parameter ECTt−1 is the error correction term, φ is the speed adjustment

coefficient, ζ is the disturbance term in equation 3.

Table 6: ARDL approach long-run and short-run test results

Dependent Variable: ln𝐂𝐎𝟐

Long-run results

Variables Coefficient Standard Error p-value

ln𝐂𝐎𝟐 0.498 0.139 0.000

lnEC 0.722 0.213 0.001

lnGDP 0.391 0.174 0.007

InTO 0.002 0.001 0.119

Short-run results

Variables Coefficient Standard Error p-value

ECMT (-1) -0.334 0.045 0.000

Table 6 shows that long run and short run coefficients, whereas lnCO2 is taken as

the dependent variables. According to the results of ARDL approach lnEC, lnGDP

and lnTO have a statistically significant impact on lnCO2. It show that, when EC

increase by 100%, CO2 emissions will increases by 72.2 % and 100% increase in

+ ∑

+ ∑




lnGDP leads to 39.1% increase in CO2 and when lnTO increase by 100%, CO2 emissions increasing 0.2%.

The estimation result of ECM is shown in Table 6. ECT (-1) coefficient is

statistically significant at 1% significance level and ECTt−1 is -0.334 with expected

sign, proposing that when emissions is under its equilibrium level, it adjusts by

almost 47% per year.

3.4. Granger Causality Test

If there is a long-term relationship between variables, causal relationship among

the variables should be estimated. Granger causality test using F statistic. Granger

causality is given as below;

∆𝐥𝐧𝐂𝐎𝟐𝐭

∆𝐥𝐧𝐄𝐂𝐭 ∝𝟏 ∝𝟐

∑𝐩

𝛃𝟏𝟏𝐢 𝛃𝟏𝟐𝐢 𝛃𝟏𝟑𝐢 𝛃𝟏𝟒𝐢 𝛃𝟏𝟓𝐢 𝛃𝟐𝟏𝐢 𝛃𝟐𝟐𝐢 𝛃𝟐𝟑𝐢 𝛃𝟐𝟒𝐢 𝛃𝟐𝟓𝐢

∆𝐥𝐧𝐂𝐎𝟐𝐭−𝐢

∆𝐥𝐧𝐄𝐂𝐭−𝐢

∆𝐥𝐧𝐆𝐃𝐏𝐭 [ ∆𝐥𝐧𝐓𝐎𝐭 ]

𝛗𝟏 𝛗𝟐

= [∝𝟑] +

∝𝟒 𝛆𝟏 𝛆𝟐

[ ] 𝛃𝟑𝟏𝐢 𝛃𝟑𝟐𝐢 𝛃𝟑𝟑𝐢 𝛃𝟑𝟒𝐢 𝛃𝟑𝟓𝐢

𝛃𝟒𝟏𝐢 𝛃𝟒𝟐𝐢 𝛃𝟒𝟑𝐢 𝛃𝟒𝟒𝐢 𝛃𝟒𝟓𝐢

+ ∆𝐥𝐧𝐆𝐃𝐏𝐭−𝐢 [ ∆𝐥𝐧𝐓𝐎𝐭−𝐢 ]

[𝛗𝟑] 𝐄𝐂𝐌𝐭−𝟏 + [𝛆𝟑

] (4)

𝛗𝟒 𝛆𝟒

The ∝’, β’s and φ’s are the parameters are estimated. ECMt−1 represents the one

period lagged error-term derived from the cointegration vector and the ε’s are

serially independent with mean zero and finite covariance matrix in Equation 4.

Table 7: Granger F-test results

∆ln𝐂𝐎𝟐 ∆lnEC ∆lnGDP ∆lnTO

∆ln𝐂𝐎𝟐 (3.232)** (0.379) (0.630)

∆lnEC (0.000) (0.292) (2.788)***

∆lnGDP (0.527) (0.463) (2.383)***

∆lnTO (2.594)*** (1.998) (2.625)***

x→y means x Granger causes y.

** and *** denotes the statistical significance at the 5% and %10.

The Granger causality results are represented in Table 7 The numbers in the table

represents F-statistic test results. The numbers in parentheses give the probability of

F-statistic. According to Table 7; there are three unidirectional granger causality

relationships exist among variables running from electricity consumption to CO2 emissions, from CO2 emissions to trade openness, and from trade openness to

electricity consumption. In addition to the unidirectional causal relationships, the test

results indicate that there is one bi-directional causality relationship exists among

variables, between trade openness and GDP per capita. Figure 1 illustrates granger

causality relationship among the variables.

𝐢= 𝟏




Figure 1: Granger Causality Relationship between the Variables

3.5. Cusum and Cusumsq Test

The Cusum and Cusumsq test are applied to test the stability of the long term

coefficients used to obtain ECM, since instability problem might be the result of

incomplete modeling of short-run dynamics (Laidler, 1993). According to the test

results it can be stated that the estimated coefficients are stable in the long run since

the plots of both tests fluctuate inside the 5% critical bounds. Figure 2 shows the plot

of Cusum and Cusumsq Tests.

Figure 2: Cusum and Cusumsq Tests

20

15

10

5

0

-5

-10

-15

-20

1975 1980 1985 1990 1995 2000 2005 2010

1.4

1.2

1.0

0.8

0.6

0.4

0.2

0.0

-0.2

-0.4

1975 1980 1985 1990 1995 2000 2005 2010

CUSUM of Squares 5% Significance

CO2 Emissions Electricity

Consumption

GDP Trade Openness

CUSUM 5% Significance




CONCLUSION AND POLICY RECOMMENDATIONS

This papers aims to investigate the interaction between CO2 emissions, electricity

consumption, economic growth and trade openness for the case of Turkey by using

time series data covering the years between 1962 and 2013.

To be able to investigate the interaction among variables the empirical analysis

employed starting with the tests for stationary of the variables. Since the variables

show mix order of integration, the bounds test based on ARDL approach is employed

to estimate the long run relationship among the variables. According to the Bounds

test results it is found that the electricity consumption, economic growth and trade

openness have statistically significant and positive impact on CO2 emissions.

ECM applied to the short run deviation from its long run equilibrium. The

estimated ECT (-1) coefficient of the employed ECM is statistically significant at 1%

significance level with expected sign, proposing that when CO2 emissions is under its

equilibrium level, it adjusts by almost 33% per year.

Then, the found that long-term relationship among variables, therefore Granger

Causality Test applied. The Granger Causality Test results demonstrated the

existence of unidirectional short-run causal relationship from electricity consumption

to CO2 emissions, from CO2 emissions to trade openness, from trade openness to

electricity consumption. Additionally, there is evidence of one bi-directional

causality relationships between trade openness and GDP per capita.

Cusum and Cusumsq Test applied to coefficients are stable in the long run.

Cusum and Cusumsq Test results are evidence that coefficients in ECM are stable

over the period between 1962 and 2013.

Estimation results show that as electricity consumption increases CO2 emissions

also increase in case for Turkey. The main reason behind the increase in CO2 emissions is due to the use of fossil fuels for electricity generation since fossil fuels

have the highest share among the energy resources use to generate electricity in

Turkey. Turkey’s energy policy has a critical importance both in reducing the

environmental problems caused by CO2 emissions and in terms of economic growth.

Turkey’s energy production depends highly on foreign countries energy sources

since energy production in manufacturing sector is generated from electricity, which

is produce mostly from natural gas imported from abroad increasing cost of

production.

Turkey is one of the countries which generate highest levels of CO2 emissions.

High level of CO2 emissions depends on two reasons. First is that Turkey has high energy intensity arising from inefficient use of energy (Islatince & Haydaroğlu,

2009) and second is the dependency of Turkey on non-renewable sources instead of renewable energy sources to generate in energy, in particular electricity. Electricity is one of the main inputs in industrial manufacturing, and it is also a fundamental factor

that increases people's quality of life (Karagöl, et al., 2007). The priority of Turkey

should be to decrease energy intensity and increase the share of renewable energy

sources to generate electricity in the manufacturing industry. This can be achieved

through carbon taxation of the manufacturing sector. The carbon tax system can be

designed to promote the use of renewable energy sources to generate electricity for




the manufacturing sector. This will also decrease the importation of natural gas.

Thus, the cost of electricity generation will be decreased. Moreover, the cost of the

manufacturing industry production will be reduced in long-run.

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Zeliha Özdemir, Dönüşüm İş Sağlığı ve Güvenliğinde İnsan Kaynakları Uzman

Yardımcısıdır. Aldığı eğitim sırasıyla, Muhasebe ve Vergi Uygulamaları Ön

Lisans (Afyon Kocatepe Üniversitesi, Türkiye), Ekonomi Lisans (Lefke Avrupa

Üniversitesi, KKTC), İşletme Yüksek Lisans (Lefke Avrupa Üniversitesi, KKTC).

Uluslararası kongrede bildiri sunan Özdemir'in ilgi alanları şunlardır: Genelde

Ekonomi özelde ise Enerjidir.

Zeliha Özdemir, is an Assistant of Human Resource Management in

Transformation Health Safety Environment. Her education is respectively

Associate Degree of Accounting and Tax Practices (University of Afyon

Kocatepe, Turkey), Bachelor of Economics (European University of Lefke,

TRNC), Master of MBA (European University of Lefke, TRNC). She has a paper

presented in an international congress. Her research interests consist of

economics and energy.

Asst. Prof. Dr. Demet Beton Kalmaz, is a lecturer in European University of

Lefke of the Faculty of the Economics and Administrative Sciences. Her

education is respectively Bachelor of Sociology (University of Hacettepe,

Ankara, Turkey), Master of Economics (University of Leeds, Leeds, UK), and

PhD. in Economics (Eastern Mediterranean University, Famagusta, TRNC).

She has several papers presented in national and international congresses and

articles published. Her research interests consist of energy, labor market and

general economics topics.

Yrd. Doç. Dr. Demet Beton Kalmaz, Lefke Avrupa Üniversitesi Ekonomi

Bölümü öğretim üyesidir. Aldığı eğitim sırasıyla Sosyoloji Lisans (Hacettepe

Üniversitesi, Ankara, Türkiye), Ekonomi Yüksek Lisans (University of Leeds,

Leeds, UK), ve Ekonomi Doktora’dır (Doğu Akdeniz Üniversitesi, Mağusa,

KKTC). Ulusal ve uluslararası kongrelerde bildiri sunan ve makale çalışması

bulunan Kalmaz'ın ilgi alanları şunlardır: Genelde ekonomi özelde ise enerji,

emek piyasası ve uluslararası ekonomi konularıdır.

http://data.worldbank.org/indicator/EG.ELC.COAL.ZS

the impact of turkish manufacturing industry on co2 …

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